DocumentCode :
643435
Title :
Multiple classifiers systems with granular neural networks
Author :
Kumar, D. Arun ; Meher, Saroj K.
Author_Institution :
Syst. Sci. & Inf.Unit, Indian Stat. Inst., Bangalore, India
fYear :
2013
fDate :
26-28 Sept. 2013
Firstpage :
1
Lastpage :
5
Abstract :
Hybridization of neural networks and fuzzy sets has proved its efficiency in solving different pattern classification tasks, which led to the development of granular neural networks (GNNs). GNN works with the principles of granular computing and basically operates on granules of information. The present paper proposes an efficient multiple classifier system (MCS) framework with different guiding rules based GNNs. The performance of the proposed MCS is demonstrated and its superiority over individual GNNs is justified with remote sensing data for five land use/cover classes. Conventional back propagation algorithm is used to train the networks.
Keywords :
backpropagation; fuzzy set theory; neural nets; pattern classification; GNN; MCS; back propagation algorithm; fuzzy sets; granular computing; granular neural networks; information granules; land cover classes; land use classes; multiple classifier systems; neural network hybridization; pattern classification tasks; remote sensing data; Accuracy; Artificial neural networks; Biological neural networks; Fuzzy sets; Remote sensing; Topology; Pattern recognition; granular neural network; land cover classification; neural network; remote sensing image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Computing and Control (ISPCC), 2013 IEEE International Conference on
Conference_Location :
Solan
Print_ISBN :
978-1-4673-6188-0
Type :
conf
DOI :
10.1109/ISPCC.2013.6663450
Filename :
6663450
Link To Document :
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